For every bucket of predicted probability, what did the model actually hit? A well-calibrated model has actual win rate ≈ predicted prob. A negative gap means the model is overconfident in that bucket (dangerous); positive gap means it's underpredicting (safe). Rows turn red when |gap| > 5pp AND N ≥ 10.
Combined "all sports" is rarely meaningful — sports differ in market efficiency, signal availability, and base rates. Use the chips to drill into a single sport. N<50 (red) means the calibration is brittle; N≥200 (green) is trustworthy.
Filter: window=90d · sport=nfl_preseason
| Predicted-prob bucket | N | Mean predicted | Actual win rate | Gap (actual − predicted) | Brier |
|---|---|---|---|---|---|
| <50% | 11 | 49.5% | 36.4% | -13.2pp | 0.249 |
| 50-55% | 67 | 52.1% | 61.2% | +9.1pp | 0.248 |
| 55-60% | 40 | 57.4% | 52.5% | -4.9pp | 0.252 |
| 60-65% | 19 | 61.7% | 63.2% | +1.5pp | 0.238 |
| 65-70% | 5 | 67.1% | 100.0% | +32.9pp | 0.108 |
| 70-75% | 3 | 73.4% | 66.7% | -6.7pp | 0.224 |